A simple approach for finding estimable functions in linear models

R. K. Elswick, Chris Gennings, Vernon Chinchilli, Kathryn S. Dawson

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

In some overparameterized linear models it can be difficult to determine which parametric functions are estimable. Students in linear models courses and data analysts usually rely upon intuition and/or trial and error methods to determine estimability. We suggest reducing the design matrix to row echelon form as a means of finding the structure of all estimable functions. We illustrate the procedure with two examples.

Original languageEnglish (US)
Pages (from-to)51-53
Number of pages3
JournalAmerican Statistician
Volume45
Issue number1
DOIs
StatePublished - Jan 1 1991

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

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